Resolution-converting apparatus and method

ABSTRACT

The resolution-converting method comprises steps of applying an edge-directed interpolation to an input image and producing an intermediate image; converting a sampling rate with respect to the intermediate image and producing an output image having a predetermined resolution; and improving sharpness of the produced output image. The present invention prevents image-quality degradation factors that can occur in the conventional resolution-converting method as well as obtains an output image having a resolution of a desired size.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims benefit under 35 U.S.C. §119 from Korean PatentApplication No. 2004-63605 filed on Aug. 12, 2004 in the KoreanIntellectual Property Office, the entire content of which isincorporated herein by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention generally relates to a resolution-convertingapparatus and method. More particularly, the present invention relatesto a resolution-converting apparatus and method preventing theoccurrence of factors deteriorating image quality and converting aninput image into an output image having a desired resolution size.

2. Description of the Related Art

The digital display devices, such as liquid crystal displays (LCDs) orthe like, have a fixed screen resolution depending on products, unlikeanalog display devices, such as cathode ray tubes (CRTs), so resolutionconversion is essential to convert the diverse resolutions of inputimages into a screen resolution of a display device. In particular, withthe advent of high-definition televisions (HDTVs), the resolutionconversion becomes more essential to display the images of existingstandard definition-class resolution on a high-definition-class TVsystem.

However, the conventional methods used for such resolution conversionsintroduces image artifacts around image edges greatly affecting imageperceptions through human eyes. There are the typical image artifactssuch as the jagging artifacts of producing a sawtoothed pattern aroundimage edges resulting from resolution conversions, the blurringartifacts of producing poor sharpness due to the resolution conversions,the ringing and aliasing artifacts around image edges due to poor filtercharacteristics, and so on. The conventional technologies related to theresolution conversions are mainly classified into a method using linearfiltering technologies and a method using non-linear filteringtechnologies. That is, the method disclosed in U.S. Pat. Nos. 5,889,895and 5,671,298 and U.S. Patent Application No. 2002/0140854 carry outresolution conversions using the linear filtering schemes, such asbilinear and cubic interpolations. However, such methods have a problemof deteriorating the image quality of output images since the sharpnessbecomes very poor due to the insufficient reproduction of thehigh-frequency components of an input image when upsizing resolutions.In order to compensate for such a problem, U.S. patent application No.2002/0140854 discloses a method of outputting a high-definition imagesignal by identifying potential edge pixels with application of thepeaking to a low resolution signal, applying the up-conversion withrespect to the peaked image signal, and sequentially carrying out edgepixel detections, edge linking, and luminance transient improvements.However, the method enhances details and sharpness by using the existinglinear filters during scaling and applying the peaking and luminancetransient improvement functions based on the pre-processing andpost-processing approaches before and after filtering, so the methodneeds lots of computations and a complicated hardware structure,therefore limiting the performance improvements.

The methods using the non-linear filtering techniques disclosed in U.S.Pat. Nos. 5,852,470 and 5,446,804 can be divided into the directionalinterpolation method and the content-based interpolation method. Thedirectional interpolation method performs interpolation only in adirection parallel with an edge direction to prevent the edge componentsfrom producing blurring artifacts, using edge information of an image.The content-based interpolation method obtains an optimized scalingfilter coefficient through a learning process in advance, selects alearned filter coefficient depending on local characteristics of aninput image, and performs resolution conversions. Such methods providerelatively good results in the edge portions of an image, but has adisadvantage of degrading image quality in fine-textured regions.

SUMMARY OF THE INVENTION

Additional aspects and/or advantages of the invention will be set forthin part in the description which follows and, in part, will be apparentfrom the description, or may be learned by practice of the invention.

The present invention has been developed in order to solve the abovedrawbacks and other problems associated with the conventionalarrangement. An aspect of the present invention is to provide aresolution-converting apparatus and method for converting an input imageinto an output image having a resolution of a desired size whileeliminating the occurrence of image quality-degrading factors.

The foregoing and other objects and advantages are substantiallyrealized by providing a resolution-converting method including applyingan edge-directed interpolation to an input image and calculating anintermediate image; converting a sampling rate with respect to theintermediate image and calculating an output image having apredetermined resolution; and improving sharpness of the calculatedoutput image.

According to an aspect of the present invention, the edge-directedinterpolation operation includes operations of using absolutedifferences of pixels of the input image to estimate an edge direction;adaptively determining an interpolation coefficient based on a pixelvalue of a position corresponding to the estimated edge direction; usingthe edge direction and the interpolation coefficient and calculating theedge-directed interpolation value based on the edge direction andinterpolation coefficient; and adaptively blending the edge-directedinterpolation value and an interpolation value by a predetermined linerinterpolation method and calculating a final interpolation value.

The edge direction is estimated with respect to two diagonal directionsand four non-diagonal directions, and the predetermined linearinterpolation method is a bi-cubic interpolation method.

According to an aspect of the present invention, theresolution-converting method further includes determining whether aninterpolation region has a high spatial frequency, and, if the regionhas a high spatial frequency, using an interpolation value by thepredetermined linear interpolation method to calculate a finalinterpolation value, and it is determined whether the region has thehigh spatial frequency by using mean-deviated local variance in theinterpolation region.

The operation of calculating the output image includes multiplying aratio of up-samplings and down-samplings calculated according to theresolution of the input image and a predetermined resolution andcalculating the number of filter tabs; multiplying a window function bya sinc function and calculating a first-order filter coefficient by thenumber of filter tabs; using a value obtained from multiplying aGaussian function and the window function in the first-order filtercoefficient and calculating a final filter coefficient; and converting asampling rate of the intermediate image, performing filtering inhorizontal and vertical directions according to the final filtercoefficient, and calculating an output image of a predeterminedresolution.

The number of filter tabs is calculated based on an equation, that is,L=round(Max {D,U}×SmoothingAmount×nLobes−1)×2−1

where L indicates a filter length, nLobes the number of side lobes, Uand D ratios of optimized up-samplings to down-samplings, andSmoothingAmount a constant changing a cut-off frequency of the filter.

The first-order filter coefficient is calculated based on an equation.That is,

${{h\lbrack i\rbrack} = {\left\{ \frac{\sin(x)}{x} \right\} \times {{kaiser}\left( {1,\beta} \right)}}},{i = 0},1,\ldots\mspace{14mu},{L - 1},{x = {\frac{i - \frac{L - 1}{2}}{\frac{L - 1}{2}} \times \pi \times {Lobes}}}$where

$\frac{\sin(x)}{x}$denotes an ideal low-frequency bandpass function and Kaiser(1,β) is aKaiser window function.

According to an aspect of the present invention, the improving thesharpness includes picking up two representative colors in an overlappedblock of a predetermined size; increasing contrast between the picked-uprepresentative colors; transiting a pixel value existing in an edgeregion to an approximate representative color of the twocontrast-increased representative colors; and adding result values ofthe overlapped block by using a hanning window and calculating a finalpixel value in order to remove block discontinuity.

The contrast of the representative colors is adaptively increasedthrough a distance and a dot product between an input pixel and the tworepresentative colors, and the two representative colors are picked upby the K-means algorithm.

According to an aspect of the present invention, a resolution-convertingapparatus includes an edge-directed interpolation unit for applying anedge-directed interpolation to an input image and calculating anintermediate image; a linear filtering unit for converting a samplingrate with respect to the intermediate image and calculating an outputimage having a predetermined resolution; and a sharpness improvementunit for improving sharpness of the calculated output image.

According to an aspect of the present invention, the edge-directedinterpolation unit uses absolute differences of pixels of the inputimage to estimate an edge direction, adaptively determines aninterpolation coefficient based on a pixel value of a positioncorresponding to the estimated edge direction, uses the edge directionand the interpolation coefficient, calculates the edge-directedinterpolation value, adaptively blends the edge-directed interpolationvalue and an interpolation value by a predetermined liner interpolationmethod, and calculates a final interpolation value.

The edge direction is estimated with respect to two diagonal directionsand four non-diagonal directions, and the predetermined linearinterpolation method is a bi-cubic interpolation method.

The edge-directed interpolation unit, if the region has a high spatialfrequency, uses an interpolation value by the predetermined linearinterpolation method to calculate a final interpolation value, and it isdetermined whether the region has the high spatial frequency by usingmean-deviated local variance in the interpolation region.

According to an aspect of the present invention, the sharpnessimprovement unit picks up two representative colors in an overlappedblock of a predetermined size, increases contrast between the picked-uprepresentative colors, transits a pixel value existing in an edge regionto an approximate representative color of the two contrast-increasedrepresentative colors, adds result values of the overlapped block byusing a hanning window, and calculates a final pixel value in order toremove block discontinuity.

Preferably, the contrast of the representative colors is adaptivelyincreased through a distance and a dot product between an input pixeland the two representative colors, and the two representative colors arepicked up by the K-means algorithm.

BRIEF DESCRIPTION OF THE DRAWINGS

These and/or other aspects and advantages of the invention will becomeapparent and more readily appreciated from the following description ofthe embodiments, taken in conjunction with the accompanying drawings ofwhich:

FIG. 1 is a block diagram of a resolution-converting apparatus accordingto an embodiment of the present invention;

FIG. 2 illustrates interpolated pixels created by interpolation ofedge-directed interpolation unit of FIG. 1;

FIG. 3 is a flowchart of operations of the edge-directed interpolationunit of FIG. 1;

FIG. 4A and FIG. 4B illustrate direction estimation in the edge-directedinterpolation;

FIG. 5A to FIG. 5F illustrate a decision method of an interpolationcoefficient;

FIG. 6 illustrates a block distribution;

FIG. 7 illustrates a decision method of an interpolation coefficient inthe second interpolation;

FIG. 8 illustrates general sampling conversions;

FIG. 9 is a block diagram of a sampling-rate conversion circuit in thelinear filtering unit of FIG. 1;

FIG. 10 is a flowchart of operations of the sharpness improvement unitof FIG. 1;

FIG. 11 illustrates errors between actual edge components and estimatededge components;

FIG. 12 illustrates improvement of contrast between representativecolors;

FIG. 13 illustrates a method improving contrast between representativecolors;

FIG. 14 illustrates a final image processed in the sharpness improvementunit of FIG. 1; and

FIG. 15 illustrates block discontinuity removal.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Reference will now be made in detail to the embodiments of the presentinvention, examples of which are illustrated in the accompanyingdrawings, wherein like reference numerals refer to the like elementsthroughout. The embodiments are described below to explain the presentinvention by referring to the figures.

Hereinafter, the present invention will be described in detail withreference to the accompanying drawings.

FIG. 1 is a block diagram of a resolution-converting apparatus accordingto an embodiment of the present invention. In FIG. 1, theresolution-converting apparatus includes an edge-directed interpolationunit 100, a linear-filtering unit 200, and a sharpness improvement unit300. The edge-directed interpolation unit 100, the linear-filtering unit200, and the sharpness improvement unit 300 are not software.

The edge-directed interpolation unit 100 applies the edge-directedinterpolation to an input image to upsize the resolution two timeshorizontally and vertically, and produces an intermediate image freefrom jagging artifacts that can occur during the resolution conversion.The linear filtering unit 200 applies linear FIR (Finite ImpulseResponse) filtering to the intermediate image output from theedge-directed interpolation unit 100 while changing a sampling rate,thereby producing an image having the same resolution as a desiredresolution, that is, the linear filtering unit 200 produces an outputimage having the same resolution as a desired resolution, minimizing theoccurrence of ringing artifacts and aliasing artifacts in such a manner.Since the edge-directed interpolation unit 100 and the linear filteringunit 200 degrade sharpness during the resolution conversions, thesharpness improvement unit 300 compensates for the sharpness andproduces a final image.

The resolution-converting apparatus as constructed above can be moreclearly understood with detailed description on the edge-directedinterpolation unit 100, the linear filtering unit 200, and the sharpnessimprovement unit 300.

FIG. 2 illustrates interpolated pixels generated by the interpolation ofthe edge-directed interpolation unit 100 of the resolution-convertingapparatus of FIG. 1. In FIG. 2, the edge-directed interpolation of theedge-directed interpolation unit 100 is performed through a two-stepprocess of first and second interpolations. The first interpolationproduces interpolated pixels at odd lines and columns through theedge-directed interpolation, and the second interpolation produces theremaining interpolated pixels through the edge-directed interpolation.The first and second interpolations change only positions of originalpixels for reference, and use the same edge-directed interpolation toproduce interpolated pixels.

FIG. 3 is a flowchart for explaining the operations of the edge-directedinterpolation unit 100 of the resolution-converting apparatus of FIG. 1.In FIG. 3, the edge-directed interpolation unit 100 first determines aninterpolation coefficient and edge direction (S400), and applies theedge-directed interpolation and calculates an edge-directedinterpolation value depending on the determined interpolationcoefficient and edge direction (S405). The edge-directed interpolationapplied in the edge-directed interpolation unit 100 estimates total sixedge directions including two diagonal directions shown in FIG. 4A andfour non-diagonal directions shown in FIG. 4B.

The edge-directed interpolation unit 100 calculates an edge-directedinterpolation value using one of the diagonal direction interpolationY_(EDI-Diag) and the non-diagonal direction interpolationY_(EDI-NonDiag) as follows, depending on an edge direction and aninterpolation coefficient that are determined by a process to be laterdescribed.

[Equation 1]

$\begin{matrix}{Y_{EDI\_ Diag} = {\sum\limits_{i = 0}^{3}{\alpha_{i} \times X_{i}}}} & \left\lbrack {{Equation}\mspace{14mu} 1} \right\rbrack \\{{\sum\limits_{i = 0}^{3}\alpha_{i}} = 1} & \; \\{Y_{{EDI\_ Non}{\_ Diag}} = {\sum\limits_{i = 4}^{11}{\alpha_{i} \times X_{i}}}} & \left\lbrack {{Equation}\mspace{14mu} 2} \right\rbrack \\{{\sum\limits_{i = 4}^{11}\alpha_{i}} = 1} & \;\end{matrix}$

The edge-directed interpolation unit 100 uses one of the diagonaldirection interpolation of Equation 1 and the non-diagonal directioninterpolation of Equation 2. In Equations 1 and 2, the sum of eachdirection interpolation coefficient α₁ becomes 1.

A luminance signal with respect to an RGB signal of each pixel of aninput image is obtained through Equation 3, the interpolationcoefficient used for the edge-directed interpolation is determined basedon the luminance signal, and the determined interpolation coefficient isequally applied to the R, G, and B pixels at the same spatial position.Lightness=0.3×R+0.59×G+0.11×B   [Equation3]

An interpolation coefficient of a diagonal direction is determined byusing absolute differences between original pixels of an input imagepositioned along the diagonal direction. The absolute values betweeninput pixels used to determine the interpolation coefficients of thediagonal direction are shown in FIG. 5A and FIG. 5B, and shown arrowsindicate an absolute difference value between two pixels. In thedetermination of an interpolation coefficient, the sum of absolutedifference values between pixels existing in 45° and −45° directions isdetermined in Equation 4 as below, and, through calculated Dir₀ andDir₁, an edge direction is finally estimated in a direction having aminimum value.

$\begin{matrix}\begin{matrix}{{Dir}_{0} = {a + b + c + d + e}} \\{= {{{{P\left( {{i - 1},{j + 1}} \right)} - {P\left( {{i - 3},{j - 1}} \right)}}} +}} \\{{{{P\left( {{i - 1},{j + 1}} \right)} - {P\left( {{i + 1},{J + 3}} \right)}}} +} \\{{{{P\left( {{i - 1},{j - 1}} \right)} - {P\left( {{i + 1},{j + 1}} \right)}}} +} \\{{{{P\left( {{i - 1},{j - 3}} \right)} - {P\left( {{i + 1},{j - 1}} \right)}}} +} \\{{{P\left( {{i - 1},{j - 1}} \right)} - {P\left( {{i + 3},{j + 1}} \right)}}}\end{matrix} & \left\lbrack {{Equation}\mspace{14mu} 4} \right\rbrack \\\begin{matrix}{{Dir}_{1} = {a^{\prime} + b^{\prime} + c^{\prime} + d^{\prime} + e^{\prime}}} \\{= {{{{P\left( {{i - 3},{j + 1}} \right)} - {P\left( {{i - 1},{j - 1}} \right)}}} +}} \\{{{{P\left( {{i - 1},{j - 1}} \right)} - {P\left( {{i + 1},{j - 3}} \right)}}} +} \\{{{{P\left( {{i - 1},{j + 1}} \right)} - {P\left( {{i + 1},{j - 1}} \right)}}} +} \\{{{{P\left( {{i - 1},{j + 3}} \right)} - {P\left( {{i + 1},{j + 1}} \right)}}} +} \\{{{P\left( {{i + 1},{j - 1}} \right)} - {P\left( {{i + 3},{j - 1}} \right)}}}\end{matrix} & \left\lbrack {{Equation}\mspace{14mu} 5} \right\rbrack\end{matrix}$

Equation 6 to Equation 8 as below are used to determine interpolationcoefficients (α₀,α₁,α₂,α₃) of four diagonal directions through themagnitude comparison of the Dir₀ and Dir₁. That is, if the sum ofabsolute difference values of two diagonal directions is 0, it isconsidered plain pixels having no edge, and the values of allinterpolation coefficients are determined to 0.25 as shown in Equation6.if Dir₀+Dir₁=0α₀=α₁=α₂=α₃=0.25   [Equation 6]

If the sum of the absolute difference values of the two diagonaldirections is not 0 and the absolute difference value of −45° issmaller, the interpolation coefficient is determined as shown inEquation 7 as below through the magnitude comparison with the absolutedifference value of the 45° direction.

[Equation 7] if (Dir₀ + Dir₁ > 0 and Dir₁ ≦ Dir₀) { if (2 × Dir₀ > Dir₁)α₀ = α₃ = 0.3, α₁ = α₂ = 0.2 else α₀ = α₃ = 0.5, α₁ = a₂ = 0.0 }

If the absolute difference value of the 45° direction is smaller, theinterpolation coefficient is determined as shown in Equation 8 as belowin the same manner as the −45° direction.

[Equation 8] if (Dir₀ + Dir₁ > 0 and Dir₁ ≦ Dir₀) { if (2 × Dir₁ > Dir₀)α₀ = α₃ = 0.2, α₁ = α₂ = 0.3 else α₀ = α₃ = 0.0, α₁ = α₂ = 0.5 }

An interpolation coefficient of the non-diagonal direction isdetermined, with different reference pixel position, by applying thesame method as the decision of an interpolation coefficient of thediagonal direction. FIG. 5C to FIG. 5F show pixel groups used for theedge direction decision in the non-diagonal direction. First, if anaverage value of absolute values in four directions is 0, it isconsidered a plain region, and, as shown in Equation 9 as below, all theinterpolation coefficients of eight pixels are determined to 0.125.Avg_(Dir)=(Dir₂+Dir₃+Dir₄+Dir₅)/4if Avg_(Dir)=0α₄=α₅=α₆=α₇=α₈=α₉=α₁₀=α₁₁=0.125   [Equation 9]

Meanwhile, the interpolation coefficient is set to 0.5 with respect toonly two pixels in a direction having a smallest value among fournon-diagonal directions Dir2, Dir3, Dir4, and Dir5, and the rest is allset to 0, in order to determine an interpolation coefficient. Equation10 as below shows the decision of an interpolation coefficient in casethat the direction Dir2 has the smallest value.if min{Dir₂, Dir₃, Dir₄, Dir₅}=Dir₂α₄=α₈=0.5, α₅=α₆=α₇=α₉=α₁₀=α₁₁=0   [Equation 10]

The following equation is applied to determine a final edge directioninterpolation value at a current interpolation pixel position, that is,at a pixel to be interpolated, by using maximum, minimum, and averagevalues of absolute difference values used for the interpolationcoefficient decision in each edge direction, so the edge directioninterpolation value is calculated by using any of Equation 1 andEquation 2.if (Avg{Dir₂, . . . , Dir₅}×Min{Dir₀,Dir₁}<Max{Dir₀,Dir₁}×Min{Dir₂, . .. , Dir₅}) Y _(EDI) =Y _(EDI-Diag)   [Equation 11]if (Avg{Dir₂, . . . , Dir₅}×Min {Dir₀,Dir₁}≧Max{Dir₀,Dir₁}×Min{Dir₂, . .. , Dir₅}) Y _(EDI) =Y _(EDI) _(—) _(Non) _(—) _(Diag)   [Equation 12]

Next, it is determined whether the position is in a region of highspatial frequency (S410). If not in the region of high spatialfrequency, the edge-directed interpolation value and a bi-cubicinterpolation value are blended so that a final interpolation value iscalculated (S415). That is, in case that the edge-directed interpolationis used and the edge direction is dominant, the excellent interpolationresult can be generally obtained where jagging artifacts are removedfrom edges. In case that a non-dominant direction such as a textureregion exists, adverse effect is exerted on the interpolation result.Therefore, with respect to such a region, the linear interpolationmethod such as bi-cubic interpolation rather provides a better result inthe aspect of image quality. Accordingly, if a region does not have ahigh spatial frequency, the aforementioned image quality is preventedfrom being degrading by blending the final direction interpolation valueand the bi-cubic interpolation value (S410 and S415), which can beexpressed in Equation 13 as follows.R _(out) =R _(biCubic)×(1−fBlend)+fBlend×R _(EDI)G _(out) =G _(biCubic)×(1−fBlend)+fBlend×G _(EDI)B _(out) =B _(biCubic)×(1−fBlend)+fBlend×B _(EDI)   [Equation 13]

In Equation 13, fBlend indicates a value determining a blending extent,having a value between 0≦fBlend≦1. The fBlend constant is determinedbased on Equation 14 if a final edge direction is the diagonaldirection, or based on Equation 15 if the final edge direction is thenon-diagonal direction, by using absolute difference values of Dir0 toDir5 obtained in the edge direction decision steps.if Min(Dir₀, Dir₁)×2>Max(Dir₀, Dir₁) fBlend=0.25else if Min(Dir₀,Dir₁)×4>Max(Dir₀,Dir₁) fBlend=0.5else fBlend=1.0   [Equation 14]if Min(Dir₂, . . . Dir₅)×2>Avg(Dir₂, . . . , Dir₅) fBlend=0.25else if Min(Dir₂, . . . , Dir₅)×4>Avg(Dir₂, . . . , Dir₅) fBlend=0.5else fBlend=1.0   [Equation 15]

That is, since the region has a distinct edge direction if a sum ofminimum absolute difference values determining a final edge direction isrelatively larger than a sum of absolute difference values indicatingdifferent directions, the fBlend value is increased to more reflect theedge-directed interpolation value with respect to a final interpolationresult value, so as to remove the jagging artifacts from an edge.

Such a blending method brings an excellent result with respect to mostimages, but, in the regions having a very high spatial frequency, mostedge-directed interpolations based on minimum absolute differences havea high possibility of errors in estimating an edge direction, and thelinear blending itself has a limitation in preventing image-qualitydegradation caused by the edge-directed interpolation.

There can be a method of increasing the number of edge directionsestimated for preventing such degradation. However, since the edgeintervals become tightly close so that the precision of the edgedirection estimation using minimum absolute difference values as toambient pixels is degraded, the effect can be considered to beinsignificant compared to the increase of a computation amount.Therefore, the edge-directed interpolation unit 100 detects a regionhaving a high spatial frequency and compulsorily applies the 4-tabbi-cubic interpolation result (fBlend=0) in case of pixels classified toa high-frequency region (S410 and S420).

Furthermore, variance is used to detect a high spatial frequency, butusing the variance has a disadvantage because information itself fromthe variance does not match with human vision characteristics as well asthe variance does not give information about an exact spatial frequency.Accordingly, the use of variance degrades the precision in detecting theregions having a high spatial frequency that bring about thedeterioration of image quality due to the edge-directed interpolation.FIG. 6 is a view for showing four blocks each having the same variancevalue of 5625 and a different spatial frequency. As shown in FIG. 6, theblocks can have the same variance value and different spatialfrequencies. In order to solve such a problem, the edge-directedinterpolation unit 100, instead of considering each pixel of a block,calculates variance by using differences as to average values ofneighboring pixels of each pixel based on Equation 16 as below, anddetermines whether the block is a region having a high spatialfrequency. The consideration of such mean-deviated local variance has anadvantage of precisely expressing the variance of pixel value variationin a block.

$\begin{matrix}{\begin{matrix}{{P_{mean}\left( {i,j} \right)} = {\frac{1}{8} \times \left\lbrack {{P\left( {{i - 1},{j - 1}} \right)} + {P\left( {{i - 1},j} \right)} +} \right.}} \\{{P\left( {{i - 1},{j + 1}} \right)} + {P\left( {i,{j - 1}} \right)} +} \\{{P\left( {i,{j + 1}} \right)} + {P\left( {{i + 1},{j - 1}} \right)} +} \\\left. {P\left( {{i + 1},j} \right)} \right\rbrack\end{matrix}{V_{ar} = {\frac{1}{N \times N}{\sum\limits_{i = 1}^{N - 1}{\sum\limits_{j = 1}^{N - 1}\left\lbrack {{P\left( {i,j} \right)} - {P_{mean}\left( {i,j} \right)}} \right\rbrack^{2}}}}}} & \left\lbrack {{Equation}\mspace{14mu} 16} \right\rbrack\end{matrix}$

Equation 16 results in mean-deviated local deviances (a) 395.5, (b)1186.5, (c) 2768.5, and (d) 12656.5, respectively, with respect to thefour blocks having different spatial frequencies, as shown in FIG. 6,when felt or perceived by human eyes, so the values increase as thespatial frequencies go higher. Therefore, considering the block as aregion having a high spatial frequency if the value is larger than athreshold value, the edge-directed interpolation unit 100 uses themean-deviated local deviances to force the bi-cubic interpolation to beapplied, so as to prevent the image-quality deterioration due to theedge-directed interpolation in the region having a very high spatialfrequency.

If the first interpolation is completely done, the edge-directedinterpolation unit 100 performs the second interpolation (S425). As forthe second interpolation, the edge-directed interpolation 100 appliesthe edge-directed interpolation itself, but it is different that areference pixel is 45° rotated in the clockwise direction as shown inFIG. 7.

The linear filtering unit 200 performs a sampling rate conversionprocess to obtain an output image having an intended resolution withrespect to an intermediate image with jagging artifacts minimized atedges after the edge-directed interpolation unit 100 performs theedge-directed interpolation. FIG. 8 illustrates a general sampling rateconversion process. First, an intermediate-converted image is obtainedthat has L-times more samplings through up-samplings, and, second,output samplings are obtained that has L/M times an input image samplingrate through a down-sampling process for obtaining an output imagehaving an intended resolution. The sampling rate conversion to convertresolution, that is, the function of a re-sampling system is generallydetermined by a low-pass filter, and the linear filtering unit 200 usesthe windowing technique facilitating the determination of filtercharacteristics even though diverse techniques exist for designing FIRfilters for improving the performance of a sampling rate converter,which can be expressed in Equation 17 as follows:h(n)=h _(d)(n)×w(n)   [Equation 17]

In Equation 17, h(n) denotes an impulse response of a finally designedfilter, h_(d)(n) an impulse response of an ideal filter, and w(n) awindow function, respectively.

Since the impulse response of the ideal filter has values of up to ±∞ inthe time domain so that the windowing filter design method can not beactually implemented, a windowing function of a definite length ismultiplied to obtain an impulse response of a definite length that theideal impulse response is cut off. Since the multiplication in the timedomain becomes a convolution in the frequency domain, the transferfunction of a filter to be designed becomes a convolution of a frequencyresponse of an ideal low-pass filter and a Fourier-Transformed value ofa window function. The transition bandwidth of a finally designed filterobtained by the above method is determined by the main-lobe width of theFourier Transform spectrum of a window function, and the ripples of thepass band and the stop band are determined by the magnitude of the sidelobe of the window function. Even though diverse window functions existas window functions used for the windowing method, the linear filteringunit 200 uses the Kaiser window as follows that facilitates theside-lobe magnitude controls.

$\begin{matrix}\begin{matrix}{{{w\lbrack n\rbrack} = \frac{I_{0}\left\lbrack {\beta\left( {1 - \left\lbrack {\left( {n - d} \right)/d^{2}} \right\rbrack} \right)}^{1/2} \right\rbrack}{I_{0}(\beta)}},} & {{0 \leq n \leq M},{d = \frac{M}{2}}} \\{{{w\lbrack n\rbrack} = 0},} & {otherwise}\end{matrix} & \left\lbrack {{Equation}\mspace{14mu} 18} \right\rbrack\end{matrix}$

In Equation 18, M as a filter order determines a parameter

${d = \frac{M}{2}},$β indicates a window shape, and I₀ indicates a modified zero-orderBessel function.

The ideal filter for resolution conversion is a filter having a flatresponse in the pass band and high stop band attenuationcharacteristics, and one that suppresses the occurrence of ringingartifacts in an image with a low amplitude of the side lobe, having asmall number of the side lobes that enable the impulse response of afilter. There are blurring, aliasing, and ringing artifacts as artifactsdeteriorating the image quality that can occur upon resolutionconversion due to the difficulties in designing such an ideal filter,and the function of the filter can be determined based on the extent ofsuppressing the occurrence of such image-quality artifact components.

In general, it is difficult to design the FIR filter that can remove allof such artifact components at the same time, and the respectiveartifacts occur in trade-off relation among them. For example, if thecut-off frequency of a filter is increased to suppress the occurrence ofblurring artifacts and fully express the detailed information of animage, the severe aliasing artifacts of high-frequency components of theimage occur, leading to the degradation of anti-aliasingcharacteristics. Conversely, if the cut-off frequency of the filter islowered to improve the anti-aliasing characteristics, the anti-aliasingcharacteristics are improved, but the blurring artifacts of an image areincreased. Accordingly, controlling the occurrence of the image-qualitydegradation factors such as ringing, blurring, aliasing artifacts, thelinear filtering unit 200 uses an adaptive filter design methodfacilitating the generation of an output image quality depending uponthe application field and user's preference.

The most important factor in designing the windowing-based FIR filterdesign is known to control the transition bandwidth and the extent ofthe stop band attenuation, the transition bandwidth is controlledthrough adjusting a filter length, and the stop bandwidth attenuation isadjusted through the control of a β parameter of the Kaiser window. Ingeneral, the cut-off frequency of a filter is fixed in designing a LPFfilter, and the filter length is increased as the filter kernel in thespatial domain increases the number of side lobes, and, if the number ofside lobes is fixed, the filter length is proportional to the cut-offfrequency. That is, as the cut-off frequency is increased, the main-lobebandwidth of the designed filter kernel is decreased, and, as thecut-off frequency is decreased, the main-lobe width of the filter kernelis increased, the linear filtering unit 200 controls the transitionbandwidth by controlling a filter length through max{D,U}×SmoothingAmount for controlling the cut-off frequency of alow-pass filter and nLobes indicating the number of side lobes in thefilter kernel in order to convert a sampling rate as shown in Equation19 as follows:L=round(Max {D,U}×SmoothingAmount×nlobes−1)×2−1   [Equation 19]

In Equation 19, D and U denote values obtained from dividing M and L bya greatest common divisor K of a decimation factor M and aninterpolation factor L (that is, D=M/K, U=L/K), and max(D,U) determinesthe cut-off frequency of a filter.

A filter coefficient in the spatial domain is determined throughEquation 20 as follows, i is an argument scaling constant enabling thesinc function being an ideal low-pass filter to have the number of sidelobes as many as nLobes within a filter length (0−L−1), which controlsthe occurrence of artifacts such as ringing by controlling the number ofside lobes of the designed filter kernel.

$\begin{matrix}{{{h\lbrack i\rbrack} = {\left\{ \frac{\sin(x)}{x} \right\} \times {{kaiser}\left( {1,\beta} \right)}}},{i = 0},1,\ldots\mspace{14mu},{L - 1},{x = {\frac{i - \frac{L - 1}{2}}{\frac{L - 1}{2}} \times \pi \times {nLobes}}}} & \left\lbrack {{Equation}\mspace{14mu} 20} \right\rbrack\end{matrix}$

The filtering by such a filter design method can be implemented throughthe general poly-phase filtering, and the filtering is separatelyapplied in the width and length directions to 2D images and video imagesin order to obtain a final image.

FIG. 9 is a block diagram for showing a sampling rate conversion circuitfor 2D images and video images. The poly-phase filtering can be appliedfor such filtering in consideration of computations and hardwareintricacy.

As mentioned above, the edge-directed interpolation unit 100 and thelinear filtering unit 200 can remove the occurrence of the jagging,ringing, and aliasing artifacts being the main image-quality degradationfactors due to resolution conversion, but, can not prevent the sharpnessdegradation caused by the reduction of a high-frequency band spectrum ofan input signal occurring after the resolution conversion. In order tosolve the problem of the sharpness degradation that is most important toordinary viewers' image-quality perception, the luminance transitionimprovement (LTI), the detail enhancement, and the like are used toimprove the sharpness, but the components of aliasing and ringingartifacts and the like are also improved so that many cases can occurwhich rather increase the image-quality degradation. Therefore, thesharpness cannot be improved to the ultimate extent. In order to addresssuch a problem, it is necessary to remove the components of ringingartifacts existing in an image having an improved resolution through aresolution conversion technique, for which the sharpness improvementunit 300 uses a sharpness improvement technique that can maximize thesharpness improvement extent through additional removal of such residualcomponents of ringing artifacts.

FIG. 10 is a flowchart of the operations of the sharpness improvementunit 300 of FIG. 1. In FIG. 10, the sharpness improvement unit 300drives the sharpness improvement method based on the overlapped blocks,and, after calculating two representative colors every block, increasesthe contrast between the calculated representative colors (S500 andS505). The sharpness improvement unit 300 picks up the representativecolors based on the K-means algorithm, and, first, uses the Equation 3to obtain a luminance value with respect to the input RGB signal andpicks up as the initial representative colors the RGB values of pixelshaving the maximum and minimum luminance values in a block. Thesharpness improvement unit 300 uses block pixel information based on theinitial representative values, applies the K-means algorithm, andupdates the initial representative color values. In general, the finalrepresentative colors are obtained through the repetitive computationsin the process for updating the initial representative color values byusing the K-means algorithm, but the number of repetitive computationscan be limited to once in order to implement hardware.

FIG. 11 shows errors between a real edge component and an estimated edgecomponent in a block due to the repetitive computation limitations forupdating representative colors. Since the initial representative colorsare determined by the maximum and minimum luminance values, it can beconsidered that the two estimated representative colors indicate twocolor components consisting of edge components in a block. As shown inFIG. 11, errors occur between the real edge component and the estimatededge component due to the limitation of the repetitive computation ofthe K-means algorithm to once, and, in order to compensate for theerrors, the problem of the repetitive computation limitation conditionis solved by increasing the contrast between the two representativecolors.

After the contrast between the representative colors is increased, aninput pixel value is changed based on the two most adjacentrepresentative colors with respect to all input pixels in a block, andthe ringing component is removed (S510). After passing through ahigh-frequency component improvement block based on a simple unsharpmasking in order to improve sharpness with respect to block data fromwhich the ringing component is removed, a final image is obtained(S515).

FIG. 12 illustrates the contrast increase between the two representativecolors on the two-dimensional plane. The two points in FIG. 12 indicatetwo representative colors estimated by the K-means algorithm, and thebig ellipsoid enclosing the representative colors indicates the colordistribution of block data. The contrast-increasing direction of the tworepresentative colors is indicated in arrows for approximation to therepresentative colors of the real edge component, and the circlesindicate regions established for preventing the excessive increase ofcontrast. In a process for improving the contrast of the representativecolors, the representative color values are updated by using thedistance and the dot product between the other representative colorsdifferent from input pixels of a block with respect to onerepresentative color.

FIG. 13 depicts a contrast increase method between the representativecolors. In FIG. 13, Rep_Color0 and Rep_Color1 indicate the tworepresentative colors, and Pixel_(i) and Pixel_(k) indicate colors in apredetermined block. As for the Pixel_(i), the Equations 21 and 22 asbelow explain the selection of input colors for updating the initialrepresentative colors.

$\begin{matrix}{{Dist} = {\left( {R_{{rep}\; 0} - R_{i}} \right)^{2} + \left( {G_{{rep}\; 0} - G} \right)^{2} + \left( {B_{{rep}\; 0} - B_{i}} \right)^{2}}} & \left\lbrack {{Equation}\mspace{14mu} 21} \right\rbrack \\\begin{matrix}{{{Dot}\mspace{14mu}{Product}} = {\overset{\rightarrow}{A} \cdot \overset{\rightarrow}{B}}} \\{= {{\left( {R_{i} - R_{{rep}\; 0}} \right) \times \left( {R_{{rep}\; 1} - R_{{rep}\; 0}} \right)} +}} \\{{\left( {G_{i} - G_{{rep}\; 0}} \right) \times \left( {G_{{rep}\; 1} - G_{{rep}\; 0}} \right)} +} \\{\left( {B_{i} - B_{{rep}\; 0}} \right) \times \left( {B_{{rep}\; 1} - B_{{rep}\; 0}} \right)}\end{matrix} & \left\lbrack {{Equation}\mspace{14mu} 22} \right\rbrack\end{matrix}$

The Equation 21 indicates a distance between the input color Pixel andthe representative color Rep_Color0, and the Equation 22 indicates a dotproduct between two vectors {right arrow over (A)}, {right arrow over(B)} formed by the two representative colors and the input color. If theinput color is in a region for preventing the excessive contrastincrease or the dot product is a negative number, candidate colors arepicked up for updating the representative colors, and the candidatecolors are used to update the representative colors.if Dist<Limit_Dist & & Dot_Product<0 Use the color undate otherwise Skip  [Equation 23]

The Limit_Dist value for preventing the excessive contrast increase isdefined by Equation 24 as follows:Limit−Dist=|(R _(rep0) −R _(rep1))²+(G _(rep0) −G _(rep1))²+(B _(rep0)−B _(rep1))²|>>4   [Equation 24]

After the candidate pixels are determined by applying the abovecomputation to the two representative colors with respect to all thepixels in a block, the K-means algorithm is applied to obtain theupdated representative colors. After the computation of a finalrepresentative color having an improved contrast, the proximity ofpixels in the block is measured by neighboring representative colors andadaptively transited so that the ringing artifacts are removed. If theinput color is located in the middle of the two representative colors,the input value itself is output without color transition, and, as theinput color is closer to a neighboring representative color or the edgecomponent in a block is stronger, the extent of color transition isincreased. In order to transit colors by using the representativecolors, it is necessary to measure the edge strength and the proximitybetween the two representative colors and the current input color, andthe method of determining the edge strength is as follows:Rep−Dist=√{square root over ((R _(rep0) −R _(rep1))²+(G _(rep0) −G_(rep1))²+(B _(rep0) −B _(rep1))²)}{square root over ((R _(rep0) −R_(rep1))²+(G _(rep0) −G _(rep1))²+(B _(rep0) −B _(rep1))²)}{square rootover ((R _(rep0) −R _(rep1))²+(G _(rep0) −G _(rep1))²+(B _(rep0) −B_(rep1))²)}  [Equation 25]Dist=|R _(i) −R _(rep-i) |+|G _(i) −G _(rep-i) |+|B _(i) −B_(rep-i)|  [Equation 26]

The Equation 25 indicates a distance between the representative colors,and the Equation 26 indicates a distance between the representativecolors containing the input pixel and the current input pixel determinedupon the application of the K-means algorithm, and the strength of afinal edge component with respect to the current input block is obtainedby using the pseudo-code and the Equation 27.

$\begin{matrix}{{{{for}\mspace{14mu} i\text{:}i}=={{BlockHeight}\text{:}\mspace{14mu}{i++}}}{{{for}\mspace{14mu} j\text{:}j}=={{BlockWight}\text{:}\mspace{14mu}{j++}}}\begin{matrix}{{Dist} = {{{R_{i,j} - R_{{{rep} - i},j}}} + {{G_{i,j} - G_{{{rep} - i},j}}} +}} \\{{B_{ij} - B_{{{rep} - i},j}}}\end{matrix}{{{if}\mspace{14mu}\left( {{{Dist} \times 4} > {Rep\_ Dist}} \right){TotalDiat}}+={Dist}}{{{Edge} - {Measure}} = {{TotalDist} \times k}}} & \left\lbrack {{Equation}\mspace{14mu} 27} \right\rbrack\end{matrix}$

In order to obtain a color adjacent to the current input color of thetwo representative colors, a distance between each representative colorand the current input color is computed as follows. Dist0 indicates adistance between the current input pixel and the representative colordetermined upon application of the K-means algorithm, and Dist1indicates a distance between the other representative colors.Dist0=(R _(i) −R _(rep0))²+(G _(i) −G _(rep0))²+(B _(i) −B _(rep0))²  [Equation 28]Dist1=(R _(i) −R _(rep1))²+(G _(i) −G _(rep1))²+(B _(i) −B _(rep1))²  [Equation 29]

The final proximity using the distance between the two representativecolors and the current input color is expressed in Equation 30 asfollows:

$\begin{matrix}{{Proximity} = {\frac{{Dist}\; 0}{{Dist}\; 1}.}} & \left\lbrack {{Equation}\mspace{14mu} 30} \right\rbrack\end{matrix}$

In Equation 30, the proximity indicating an adjacent degree is clippedto have a value between 0˜1, and the value is decreased as it comescloser to the representative color of the current input pixel. Theextent of the final color transition is increased as the edge strengthin a block is increased or a distance with a neighboring color becomessmaller, which can be obtained based on Equation 31 as follows:R _(i-mod)+=(1−Proximity)×Edge−Measure×(R _(i) −R _(rep-1))G _(i-mod)+=(1−Proximity)×Edge−Meaure×(G _(i) −G _(rep-1))B _(i-mod)+=(1−Proximity)×Edge−Meaure×(B _(i) −B _(rep-1))   [Equation31]

FIG. 14 depicts a final image processed in the sharpness improvementunit 300. In FIG. 14, the resolution-converted input image can haveresidual ringing artifacts, and, if the detail enhancement is applied bya post-process method with respect to the input image, the ringingcomponent is also improved so that the image quality can be degraded.Since the sharpness improvement unit 300 obtains the two representativecolors in a block and moves the pixel values of the block with respectto the representative colors, the sharpness improvement unit 300 removesthe ringing components at the same time of performing a function similarto the LTI or CTI. As above, a simple unsharp masking technique isapplied to the ringing component-removed block about the representativecolors to improve the sharpness, so that a final output image can beobtained that has the improved sharpness and the minimized emphasis ofthe ringing artifacts and the like.

Meanwhile, since such a sharpness improvement method is the computationbased on blocks, there can occur a discontinuity problem at theboundaries between blocks appearing in the block-based compressionmethod such as JPEG and MPEG in case that each of the independentlyprocessed blocks is output as it is. In order to improve such a problem,the sharpness improvement unit 300 operates based on an overlapped blockstructure, and removes the discontinuity that can occur at theboundaries of the processed blocks by using the Hanning window. That is,the input image is overlapped by half the entire block size to constructa block (for example, the input block is overlapped by 4 pixels withneighboring blocks in case of the size of the input block is 8×8), andenables each of the independently processed blocks to be smoothlytransited at the boundaries between neighboring blocks by using theHanning window, so as to remove the discontinuity.

FIG. 15 is a conceptual view of the smooth transition using the Hanningwindow at the boundaries between two neighboring blocks. In FIG. 15, atwo-dimensional Hanning window is multiplied by each of theindependently processed block data, (FIG. 14 is for description in theone-dimensional form) the block data multiplied by a window coefficientis added with each other to obtain a final block data. The windowfunction multiplied by each block is distributed in a form having ablock boundary direction at one side and fading in from the blockboundary direction at the other side, and the sum of two window functioncoefficients at the overlapped block boundary portion becomes 1 all thetime, so that the smooth transition without distortion can be achieved.

As aforementioned, the present invention can solve the problem ofjagging artifacts, ringing artifacts, sharpness degradation, and thelike which are image-quality degradation factors that can occur when theconventional resolution conversion method is used. Furthermore, the FIRfilter design technique used in the linear filtering unit has anadvantage of facilitating the filter design by changing filter designparameters in the spatial domain depending on application fields orusers' preference. The edge-directed interpolation filter simplifies theedge direction estimation, adaptively blends the problem of aliasingcharacteristics degradation existing in a general non-linear filter witha simple 4-tab bi-cubic interpolation result through the classificationof input images, so as to improve the anti-aliasing characteristics andminimize the jagging artifacts at edges. In particular, the sharpnessimprovement unit effectively removes residual ringing artifacts in aresolution-converted image, so as to effectively improve the sharpnessof the resolution-converted image through the application of the simpleunsharp mask as well as replace the conventional detail enhancementmethod that is independently used.

Although a few embodiments of the present invention have been shown anddescribed, it would be appreciated by those skilled in the art thatchanges may be made in these embodiments without departing from theprinciples and spirit of the invention, the scope of which is defined inthe claims and their equivalents.

1. A resolution-converting method, comprising: applying an edge-directedinterpolation to an input image and producing an intermediate image; andconverting a sampling rate with respect to the intermediate image andproducing an output image having a predetermined resolution, wherein theedge-directed interpolation comprises, a first interpolation ofgenerating interpolated pixel of a part of the input image, and a secondinterpolation of generating interpolated pixels of other remaining partof the input image.
 2. The resolution-converting method as claimed inclaim 1, wherein the edge-directed interpolation comprises: usingabsolute differences of pixels of the input image to estimate an edgedirection; adaptively determining an interpolation coefficient based ona pixel value of a position corresponding to the estimated edgedirection; using the edge direction and the interpolation coefficientand calculating an edge-directed interpolation value; and adaptivelyblending the edge-directed interpolation value and an interpolationvalue by a predetermined linear interpolation method and calculating afinal interpolation value.
 3. The resolution-converting method asclaimed in claim 2, wherein the edge direction is estimated with respectto two diagonal directions and four non-diagonal directions.
 4. Theresolution-converting method as claimed in claim 2, wherein thepredetermined linear interpolation method is a bi-cubic interpolationmethod.
 5. The resolution-converting method as claimed in claim 2,further comprising determining whether an interpolation region has ahigh spatial frequency, and, if the region has a high spatial frequency,using an interpolation value by the predetermined linear interpolationmethod to calculate a final interpolation value.
 6. Theresolution-converting method as claimed in claim 5, wherein it isdetermined whether the region has the high spatial frequency by usingmean-deviated local variance in the interpolation region.
 7. Aresolution-converting method, comprising: applying an edge-directedinterpolation to an input image and producing an intermediate image; andconverting a sampling rate with respect to the intermediate image andproducing an output image having a predetermined resolution, wherein thecalculating the output image comprises: multiplying a ratio ofup-samplings and down-samplings calculated according to the resolutionof the input image and a predetermined resolution and calculating thenumber of filter tabs; multiplying a window function by a sinc functionand calculating a first-order filter coefficient by the number of filtertabs; using a value obtained from multiplying a Gaussian function andthe window function in the first-order filter coefficient andcalculating a final filter coefficient; and converting a sampling rateof the intermediate image, performing filtering in horizontal andvertical directions according to the final filter coefficient, andcalculating an output image of a predetermined resolution.
 8. Theresolution-converting method as claimed in claim 7, wherein the numberof filter tabs is calculated based on an equation as below:L=round(Max {D,U}×SmoothingAmount×nLobes−1)×2−1, where L indicates afilter length, nLobes the number of side lobes, U and D ratios ofoptimized up-samplings to down-samplings, and SmoothingAmount a constantchanging a cut-off frequency of the filter.
 9. The resolution-convertingmethod as claimed in claim 7, wherein the first-order filter coefficientis calculated based on an equation as below:${{h\lbrack i\rbrack} = {\left\{ \frac{\sin(x)}{x} \right\} \times {{kaiser}\left( {1,\beta} \right)}}},{i = 0},1,\ldots\mspace{14mu},{L - 1},{x = {\frac{i - \frac{L - 1}{2}}{\frac{L - 1}{2}} \times \pi \times {Lobes}}}$where $\frac{\sin(x)}{x}$ indicates an ideal low-frequency bandpassfunction, and kaiser(1,β) is a Kaiser window function.
 10. Aresolution-converting method, comprising: applying an edge-directedinterpolation to an input image and producing an intermediate image;converting a sampling rate with respect to the intermediate image andproducing an output image having a predetermined resolution, andimproving sharpness of the produced output image, wherein improving thesharpness comprises: picking up two representative colors in anoverlapped block of a predetermined size; increasing contrast betweenthe picked-up representative colors; transiting a pixel value existingin an edge region to an approximate representative color of the twocontrast-increased representative colors; and adding result values ofthe overlapped block by using a Hanning window and calculating a finalpixel value in order to remove block discontinuity.
 11. Theresolution-converting method as claimed in claim 10, wherein thecontrast of the representative colors is adaptively increased through adistance and a dot product between an input pixel and the tworepresentative colors.
 12. The resolution-converting method as claimedin claim 10, wherein the two representative colors are picked up by aK-means algorithm.
 13. A resolution-converting apparatus, comprising: anedge-directed interpolation unit for applying an edge-directedinterpolation to an input image and calculating an intermediate image;and a linear filtering unit for converting a sampling rate with respectto the intermediate image and calculating an output image having apredetermined resolution, wherein the edge-directed interpolationcomprise, a first interpolation unit to generate interpolated pixels ofa part of the input image, and a second interpolation unit to generateinterpolated pixels of other remaining part of the input image.
 14. Theresolution-converting apparatus as claimed in claim 13, wherein theedge-directed interpolation unit uses absolute differences of pixels ofthe input image to estimate an edge direction, adaptively determines aninterpolation coefficient based on a pixel value of a positioncorresponding to the estimated edge direction, uses the edge directionand the interpolation coefficient, calculates the edge-directedinterpolation value, adaptively blends an edge-directed interpolationvalue and an interpolation value by a predetermined linear interpolationmethod, and calculates a final interpolation value.
 15. Theresolution-converting apparatus as claimed in claim 14, wherein the edgedirection is estimated with respect to two diagonal directions and fournon-diagonal directions.
 16. The resolution-converting apparatus asclaimed in claim 14, wherein the predetermined linear interpolationmethod is a bi-cubic interpolation method.
 17. The resolution-convertingapparatus as claimed in claim 14, wherein the edge-directedinterpolation unit, if the region has a high spatial frequency, uses aninterpolation value by the predetermined linear interpolation method tocalculate a final interpolation value.
 18. The resolution-convertingapparatus as claimed in claim 17, wherein it is determined whether theregion has the high spatial frequency by using mean-deviated localvariance in the interpolation region.
 19. A resolution-convertingapparatus, comprising: an edge-directed interpolation unit for applyingan edge-directed interpolation to an input image and calculating anintermediate image; and a linear filtering unit for converting asampling rate with respect to the intermediate image and calculating anoutput image having a predetermined resolution; and a sharpnessimprovement unit for improving sharpness of the calculated output image,wherein the sharpness improvement unit picks up two representativecolors in an overlapped block of a predetermined size, increasescontrast between the picked-up representative colors, transits a pixelvalue existing in an edge region to an approximate representative colorof the two contrast-increased representative colors, adds result valuesof the overlapped block by using a Hanning window, and calculates afinal pixel value in order to remove block discontinuity.
 20. Theresolution-converting apparatus as claimed in claim 19, wherein thecontrast of the representative colors is adaptively increased through adistance and a dot product between an input pixel and the tworepresentative colors.
 21. The resolution-converting apparatus asclaimed in claim 19, wherein the two representative colors are picked upby the-a K-means algorithm.
 22. A resolution-converting method,comprising: determining whether an interpolation region has a highfrequency; and interpolating using an edge-directed interpolation methodand a predetermined linear interpolation method if the interpolationregion has not the high frequency.
 23. The resolution-converting methodas claimed in claim 22, comprising: interpolating using only thepredetermined linear interpolation method if the interpolation regionhas the high frequency.
 24. The resolution-converting method as claimedin claim 23, wherein the predetermined linear interpolation method is abi-cubic interpolation method.